import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x=tf.placeholder("float",[None,784])
y_=tf.placeholder("float",[None,10])

def weight_variable(shape):
    initial=tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)
def bias_variable(shape):
    initial=tf.truncated_normal(shape=shape,stddev=0.1)
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

W_conv1=weight_variable([5, 5, 1, 32])
b_conv1=bias_variable([32])
x_image=tf.reshape(x, [-1,28,28,1])
h_conv1=tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1=max_pool_2x2(h_conv1)

W_conv2=weight_variable([5, 5, 32, 64])
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2=max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2=weight_variable([1024, 10])
b_fc2=bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
saver=tf.train.Saver()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range (20000):
        batch=mnist.train.next_batch(50)
        if i%1000==0:
            train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
            print('step %d,training accuracy %g'%(i,train_accuracy))
        train_step.run(feed_dict={x:batch[0], y_: batch[1], keep_prob: 0.5})
    saver.save(sess,'/home/lfz_5/Files/robot/Projects/train/model.ckpt')
    print('test accuracy %g'%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))